Toward humans as sensors: how dynamic space-time heat exposure patterns influence risk perception across the contiguous United States

Authors: Forrest Scott Schoessow*, Ohio State University, Peter D. Howe, Utah State University
Topics: Hazards, Risks, and Disasters, Hazards and Vulnerability, Human-Environment Geography
Keywords: hazards, heat, risk, perception, spatial-temporal, variation, biophysical, statistical, modeling, coupled, natural, human, systems
Session Type: Paper
Day: 4/12/2018
Start / End Time: 3:20 PM / 5:00 PM
Room: Napoleon B1, Sheraton 3rd Floor
Presentation File: No File Uploaded

Extreme heat events are the deadliest natural hazard in the contiguous United States (CONUS) and will continue to increase in both severity and frequency due to the effects of climate change. Heat waves have highly variable impacts determined by the dynamic space-time patterns of exposure, complex individual-level sensitivity factors, and the adaptive capacity of the exposed populations. Accurate, locally-relevant data on the distribution of exposure and sensitivity factors is required in order to more comprehensively assess extreme heat risk, identify vulnerable subpopulations, determine effective risk reduction strategies, and strengthen community resilience. Quantitative risk perception data can better equip policymakers, government officials, and risk managers with the information they need to more effectively allocate local hazard prevention and response resources to the most vulnerable populations in their respective risk areas. This study evaluates how localized personal experience with key meteorological and climatological factors known to be important contributors to overall heat risk (e.g. Heat Index, Daily Max. Temp., Seasonal Avg. Min. Temp) contribute to heat risk perceptions using time-stamped, georeferenced, CONUS nationally-representative, empirical survey data. A series of linear mixed effect models were constructed to estimate the effect size, directionality, and variance of exposure factors’ influence on risk perception across the CONUS. Findings demonstrate statistically significant non-random spatial patterns indicating the substantive influence of respondent interaction with localized environmental changes on complex individual evaluations of risk and contribute additional evidence suggesting “human sensors” have the capacity to assess localized environmental changes consistent with observations of local seasonal weather patterns and timing.

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